Dünnwald, M;
Betts, MJ;
Sciarra, A;
Düzel, E;
Oeltze-Jafra, S;
(2020)
Automated segmentation of the locus coeruleus from neuromelanin-sensitive 3t MRI using deep convolutional neural networks.
In: Tolxdorff, T and Deserno, TM and Handels, H and Maier, A and Maier-Hein, KH and Palm, C, (eds.)
Informatik aktuell.
(pp. pp. 61-66).
Springer: Berlin, Germany.
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Abstract
The locus coeruleus (LC) is a small brain structure in the brainstem that may play an important role in the pathogenesis of Alzheimer’s Disease (AD) and Parkinson’s Disease (PD). The majority of studies to date have relied on using manual segmentation methods to segment the LC, which is time consuming and leads to substantial interindividual variability across raters. Automated segmentation approaches might be less error-prone leading to a higher consistency in Magnetic Resonance Imaging (MRI) contrast assessments of the LC across scans and studies. The objective of this study was to investigate whether a convolutional neural network (CNN)-based automated segmentation method allows for reliably delineating the LC in in vivo MR images. The obtained results indicate performance superior to the inter-rater agreement, i.e. approximately 70% Dice similarity coefficient (DSC).
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